# -*- coding: utf-8 -*-
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from datetime import timedelta

from jms.dm.route.dm_route_local_city_tmp1 import dm__dm_route_local_city_tmp1
from jms.dm.route.dm_route_local_city_tmp2 import dm__dm_route_local_city_tmp2

dm__dm_route_local_city = SparkSqlOperator(
    task_id='dm__dm_route_local_city',
    task_concurrency=1,
    pool_slots=2,
    master='yarn',
    execution_timeout = timedelta(minutes=50),
    email=['zhangqinglin@jtexpress.com','yl_bigdata@yl-scm.com'],
    name='dm__dm_route_local_city',
    sql='jms/dm/route/dm_route_local_city/execute.hql',
    #executor_cores=2,
    executor_cores=2,
    #executor_memory='2G',
    executor_memory='3G',
    #num_executors=5,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    num_executors=6,
    conf={
        'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
        'spark.shuffle.service.enabled' : 'true',  # 动态资源 Shuffle 服务开启
          #'spark.dynamicAllocation.maxExecutors'  : 8,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.maxExecutors'             : 10,
        'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
        'spark.sql.sources.partitionOverwriteMode' : 'dynamic',  # 允许删改已存在的分区
        'spark.executor.memoryOverhead' : '2G',  # 堆外内存
        'spark.sql.shuffle.partitions' : 100,
    },
    hiveconf={
        'hive.exec.dynamic.partition' : 'true',  # 动态分区
        'hive.exec.dynamic.partition.mode' : 'nonstrict',
        'hive.exec.max.dynamic.partitions' : 400,  # 每天生成 20 个分区
        'hive.exec.max.dynamic.partitions.pernode': 400,  # 每天生成 20 个分区
    },
)

dm__dm_route_local_city << [
    dm__dm_route_local_city_tmp1,
    dm__dm_route_local_city_tmp2,
]
